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Estimating Response Modeling Methodology models

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Abstract An advanced review of Response Modeling Methodology (RMM) has recently summarized RMM core philosophy, modeling approach, and allied statistical expressions. This focus article complements the earlier review by presenting a step‐by‐step guide to estimating RMM models. The estimation procedure comprises two stages: first the median is estimated and then the rest of the RMM parameters are estimated. Three estimation procedures are presented for the latter stage: maximum likelihood, two‐moment matching, and nonlinear quantile regression. The three estimation methods, as applied to RMM, are first expounded and then demonstrated via a numerical example, using Monte‐Carlo simulated data from a γ distribution and an L4 orthogonal array design. Comparisons with generalized linear modeling and estimation, assuming γ distribution (correctly) and inverse Gaussian distribution (incorrectly), are given. A brief introduction to RMM is also provided. WIREs Comput Stat 2012, 4:323–333. doi: 10.1002/wics.1199 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Density Estimation Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical Models > Simulation Models

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Statistical Models > Simulation Models
Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods
Statistical and Graphical Methods of Data Analysis > Density Estimation

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